COMMENTARY: Promoting Early Detection of Breast Cancer and Care Strategies for Nigeria
Bibliographic record
Abstract
Breast cancer is the most common cancer in women particularly in developing countries like Nigeria, with high mortality, and economic costs. Worldwide, it is predicted that more than one million women are diagnosed with breast cancer, and more than 400,000 will die from the disease every year. A comparative integrative review of the literature from Nigeria and Canada showed that in Canada, there is positive association between wide spread education, early detection, the disease stage at diagnosis, and survival rates. In Nigeria, presentation with the advanced stage of the disease makes survival very low. The primary factors responsible for the late presentations are lack of awareness, misconceptions about breast cancer causes, and treatment outcomes. It is recommended that guidelines and policies about breast cancer early detection, care strategies, and ongoing management pathways be produced, disseminated, and adopted. The guidelines will assist practitioners and patients in making informed decisions and choices about the most appropriate health care for their specific clinical situations. The implementation of evidence-based guidelines will most likely help to improve care processes, the quality of clinical decisions and patient treatment outcome. (Afr J Reprod Health 2017; 21[2]: 18-25).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.047 | 0.038 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".